code: def infer(
      self,
      utterance: torch.Tensor,
      right_context: torch.Tensor,
      state: Optional[torch.Tensor],
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
      input = torch.cat((utterance, right_context))
      x = self.pre_conv(input)  # (T, B, D)
      x = x.permute(1, 2, 0)  # (B, D, T)
  
      if state is None:
          state = torch.zeros(
              input.size(1),
              input.size(2),
              self.state_size,
              device=input.device,
              dtype=input.dtype,
          )  # (B, D, T)
      state_x = torch.cat([state, x], dim=2)
      conv_out = self.conv(state_x)
      conv_out = conv_out.permute(2, 0, 1)  # T, B, D
      output = self.post_conv(conv_out) + input
      new_state = state_x[
          :,
          :,
          -self.state_size
          - right_context.size(0) : -right_context.size(0),
      ]
      return (
          output[: utterance.size(0)],
          output[utterance.size(0) :],
          new_state,
      )
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  from torchaudio.models.emformer import _EmformerAttention, _EmformerImpl, _get_weight_init_gains
label_desc: Use image node_id 5 for calling the _ConvolutionModule obj's underlying member method code with example usage: obj.infer(utterance, right_context, state) and returns: output,  output,  new_state